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multi.py
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multi.py
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import torch
from torch import nn
from torch.nn import init
import torch.nn.functional as F
from torch.autograd import Variable
from config import *
# vgg choice
base = {'dss': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M']}
# extend vgg choice --- follow the paper, you can change it
extra = {'dss': [(64, 128, 3, [8, 16, 32, 64]), (128, 128, 3, [4, 8, 16, 32]), (256, 256, 5, [8, 16]),
(512, 256, 5, [4, 8]), (512, 512, 5, []), (512, 512, 7, [])]}
# vgg16
def vgg(cfg, i, batch_norm=False):
layers = []
in_channels = i
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return layers
class sal_FeatLayer_last(nn.Module):
def __init__(self,in_channel,channel,k):
super(sal_FeatLayer_last,self).__init__()
self.main = nn.Sequential(nn.Conv2d(in_channel,channel,k,1,k//2),nn.ReLU(inplace=True),
nn.Conv2d(channel,channel,k,1,k//2),nn.ReLU(inplace=True),
nn.Dropout())
self.o = nn.Conv2d(channel,1,1,1)
self.o2 = nn.Conv2d(channel,1,1,1)
def forward(self, x):
y = self.main(x)
y1 = self.o(y)
y2 = self.o2(y)
return y1,y2
class seg_FeatLayer_last(nn.Module):
def __init__(self, in_channel, channel, k,cc):
super(seg_FeatLayer_last, self).__init__()
self.main = nn.Sequential(nn.Conv2d(in_channel, channel, k, 1, k // 2), nn.ReLU(inplace=True),
nn.Conv2d(channel, channel, k, 1, k // 2), nn.ReLU(inplace=True),
nn.Dropout())
self.o = nn.Conv2d(channel, cc, 1, 1)
self.o2 = nn.Conv2d(channel,1,1,1)
def forward(self, x):
y = self.main(x)
y1 = self.o(y)
y2 = self.o2(y)
return y, y1,y2
# extend vgg: side outputs
class sal_FeatLayer(nn.Module):
def __init__(self, in_channel, channel,k):
super(sal_FeatLayer, self).__init__()
#print("side out:", "k",k)
self.conv1 = nn.Sequential(nn.Conv2d(in_channel,channel,k,1,k//2), nn.ReLU(inplace=True))
self.conv2 = nn.Sequential(nn.Conv2d(channel,channel,k,1,k//2), nn.ReLU(inplace=True),nn.Dropout())
self.conv_s_ed = nn.Conv2d(channel+1, 1, 1, 1)
self.conv_s_m =nn.Conv2d(channel+1, 1, 1, 1)
def forward(self, x,seg_edge):
y = self.conv1(x)
y = self.conv2(y)
s_e = self.conv_s_ed(torch.cat([y,seg_edge],1))
#print(s_e.shape)
s_m = self.conv_s_m(torch.cat([y,s_e],1))
return (s_e,s_m)
class seg_FeatLayer(nn.Module):
def __init__(self,in_channel,channel,k,c):
super(seg_FeatLayer, self).__init__()
self.conv1 = nn.Sequential(nn.Conv2d(in_channel,channel,k,1,k//2), nn.ReLU(inplace=True))
self.conv_seg_ed = nn.Conv2d(channel+1, 1, 1, 1)
self.conv2 = nn.Sequential(nn.Conv2d(channel,channel,k,1,k//2), nn.ReLU(inplace=True),nn.Dropout())
self.conv_seg_m = nn.Conv2d(channel+1, c, 1, 1)
self.conv_sal_m = nn.Conv2d(channel+1, 1, 1, 1)
def forward(self,x,edge):
y = self.conv1(x)
seg_ed = self.conv_seg_ed(torch.cat([y,edge],1))
y = self.conv2(y)
seg_m = self.conv_seg_m(torch.cat([y,seg_ed],1))
sal_m = self.conv_sal_m(torch.cat([y,seg_ed],1))
return (torch.cat([y,seg_ed],1),seg_ed,seg_m,sal_m)
class Edge_featlayer_2(nn.Module):
def __init__(self,in_channel,channel):
super(Edge_featlayer_2,self).__init__()
self.conv1 = nn.Conv2d(in_channel, channel, 1, 1)
self.conv2 = nn.Conv2d(in_channel, channel, 1, 1)
self.merge = nn.Conv2d(2*channel,1,1)
def forward(self, x1,x2):
y1 = self.conv1(x1)
y2 = self.conv2(x2)
y1 = torch.cat([y1,y2],1)
y2 = self.merge(y1)
return y2
class Edge_featlayer_3(nn.Module):
def __init__(self, in_channel, channel):
super(Edge_featlayer_3, self).__init__()
self.conv1 = nn.Conv2d(in_channel, channel, 1, 1)
self.conv2 = nn.Conv2d(in_channel, channel, 1, 1)
self.conv3 = nn.Conv2d(in_channel, channel, 1, 1)
self.merge = nn.Conv2d(3 * channel, 1, 1)
def forward(self, x1, x2,x3):
y1 = self.conv1(x1)
y2 = self.conv2(x2)
y3 = self.conv3(x3)
y1 = torch.cat([y1, y2,y3], 1)
y2 = self.merge(y1)
return y2
def e_extract_layer():
e_feat_layers = []
e_feat_layers+=[Edge_featlayer_2(64,21)]
e_feat_layers += [Edge_featlayer_2(128, 21)]
e_feat_layers += [Edge_featlayer_3(256, 21)]
e_feat_layers += [Edge_featlayer_3(512, 21)]
e_feat_layers += [Edge_featlayer_3(512, 21)]
return e_feat_layers
# extra part
def sal_extra_layer():
feat_layers = []
feat_layers += [sal_FeatLayer(64, 128, 3)]
feat_layers += [sal_FeatLayer(128, 128, 3)]
feat_layers += [sal_FeatLayer(256, 256, 5)]
feat_layers += [sal_FeatLayer(512, 256, 5)]
feat_layers += [sal_FeatLayer(512,512,5)]
feat_layers += [sal_FeatLayer_last(512, 512, 7)]
return feat_layers
def seg_extra_layer():
feat_layers = []
feat_layers += [seg_FeatLayer(64, 128, 3,C)]
feat_layers += [seg_FeatLayer(128, 128, 3,C)]
feat_layers += [seg_FeatLayer(256, 256, 5,C)]
feat_layers += [seg_FeatLayer(512, 256, 5,C)]
feat_layers += [seg_FeatLayer(512,512,5,C)]
feat_layers += [seg_FeatLayer_last(512, 512, 7,C)]
return feat_layers
class DeconvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=4, stride=2, padding=1, batch_norm=False, dropout=True):
super(DeconvBlock, self).__init__()
self.deconv = torch.nn.ConvTranspose2d(input_size, output_size, kernel_size, stride, padding)
self.bn = torch.nn.BatchNorm2d(output_size)
self.drop = torch.nn.Dropout(0.2)
self.relu = torch.nn.ReLU(True)
self.batch_norm = batch_norm
self.dropout = dropout
def forward(self, x):
if self.batch_norm:
out = self.bn(self.deconv(self.relu(x)))
else:
out = self.deconv(self.relu(x))
if self.dropout:
return self.drop(out)
else:
return out
class D_U(nn.ModuleList):
def __init__(self,cc):
super(D_U,self).__init__()
#self.up = []
self.up = DeconvBlock(input_size=512,output_size=512,batch_norm=False)
self.up0=DeconvBlock(input_size=1025,output_size=256,batch_norm=True)
self.up1=DeconvBlock(input_size=513, output_size=256, batch_norm=True)
self.up2=DeconvBlock(input_size=513,output_size=128,batch_norm=True)
self.up3=DeconvBlock(input_size=257,output_size=128,batch_norm=False)
#self.extract0_sal_m=nn.ConvTranspose2d(513, 1, 8,8)
self.extract0_sal_e=nn.ConvTranspose2d(256, 1, 8,8)
self.extract0_seg_m = nn.ConvTranspose2d(256, cc, 8, 8)
#self.discrim=nn.ConvTranspose2d(256,1,4,4)
self.extract1_sal_m =nn.ConvTranspose2d(256, 1, 4,4)
#self.extract1_sal_e = nn.ConvTranspose2d(513, 1, 4, 4)
self.extract1_seg_m = nn.ConvTranspose2d(256, cc, 4, 4)
#self.extract2_sal_m = nn.ConvTranspose2d(257, 1, 2, 2)
self.extract2_sal_e = nn.ConvTranspose2d(128, 1, 2, 2)
self.extract2_seg_m = nn.ConvTranspose2d(128, cc, 2, 2)
self.extract3_sal_m = nn.ConvTranspose2d(257, 1, 1, 1)
#self.extract3_sal_e = nn.ConvTranspose2d(257, 1, 1, 1)
self.extract3_seg_m= nn.ConvTranspose2d(257, cc, 1, 1)
def forward(self, features):
SAL_M,SAL_E,SEG_M = [],[],[]
x = features[5]
x = self.up(x)
x = torch.cat([x,features[4]],1)
x1 = self.up0(x)
SAL_M.append(nn.Sigmoid()(self.extract0_sal_e(x1)))
SEG_M.append(nn.functional.softmax(self.extract0_seg_m(x1),1))
x2 = self.up1(torch.cat([features[3],x1],1))
SAL_M.append(nn.Sigmoid()(self.extract1_sal_m(x2)))
SEG_M.append(nn.functional.softmax(self.extract1_seg_m(x2),1))
x3 = self.up2(torch.cat([features[2],x2],1))
SAL_E.append(nn.Sigmoid()(self.extract2_sal_e(x3)))
SEG_M.append(nn.functional.softmax(self.extract2_seg_m(x3),1))
x4 = self.up3(torch.cat([features[1],x3],1))
SAL_M.append(nn.Sigmoid()(self.extract3_sal_m(torch.cat([features[0],x4],1))))
SEG_M.append(nn.functional.softmax(self.extract3_seg_m(torch.cat([features[0],x4],1)),1))
return SAL_E,SAL_M,SEG_M
# DSS network
class DSS(nn.Module):
def __init__(self, base,sal_feat_layers, e_feat_layers,seg_featlayers):
super(DSS, self).__init__()
self.extract = [3, 8, 15, 22, 29]
self.e_extract = [1,3,6,8,11,13,15,18,20,22,25,27,29]
#print('------connect',connect)
self.base = nn.ModuleList(base)
self.sal_feat = nn.ModuleList(sal_feat_layers)
self.e_feat =nn.ModuleList(e_feat_layers)
self.seg_feat = nn.ModuleList(seg_featlayers)
self.up =nn.ModuleList()
self.up1 = nn.ModuleList()
self.up2 = nn.ModuleList()
self.up3 = nn.ModuleList()
self.up4 = nn.ModuleList()
self.up5 = nn.ModuleList()
self.fuse =nn.Conv2d(5,1,1,1)
#self.up.append(nn.Conv2d(1,1,1))
k = 1
for i in range(5):
self.up.append(nn.ConvTranspose2d(1, 1, k,k))
k = 2 * k
k1 = 1
for i in range(6):
self.up1.append(nn.ConvTranspose2d(1, 1, k1, k1))
k1 = 2 * k1
k2 = 1
for i in range(6):
self.up2.append(nn.ConvTranspose2d(1, 1, k2, k2))
k2 = 2 * k2
k3 = 1
for i in range(5):
self.up3.append(nn.ConvTranspose2d(1, 1, k3, k3))
k3 = 2 * k3
k4 = 1
for i in range(6):
self.up4.append(nn.ConvTranspose2d(C, C, k3, k3))
k4 = 2 * k4
k5 = 1
for i in range(6):
self.up5.append(nn.ConvTranspose2d(1, 1, k3, k3))
k5 = 2 * k5
self.pool = nn.AvgPool2d(3, 1, 1)
#self.pool2 =nn.AvgPool2d(3, 1, 1)
def forward(self, x1,label):
#print(self.e_feat)
SEG_M, SEG_E, FF, SEG_SAL_M, xx2, xx3, xx, EDGES, SAL_E, SAL_M =[],[],[],[],[],[],[],[],[],[]
#####seg
if label =='seg':
num = 0
for k in range(len(self.base)):
#print(k)
x1 = self.base[k](x1)
if k in self.e_extract:
xx.append(x1)
#edges.append()
#print(k,x.size())
if k in self.extract:
if num<2:
edge =self.e_feat[num](xx[2*num],xx[2*num+1])
else:
edge = self.e_feat[num](xx[num*3-2],xx[num*3-1],xx[num*3])
#edges.append(edge)
EDGES.append(edge)
(Y,seg_ed,seg_m,sal_m)=self.seg_feat[num](x1,edge)
(s_e, s_m) = self.sal_feat[num](x1, seg_ed)
FF.append(Y)
SEG_E.append(seg_ed)
SEG_M.append(seg_m)
SEG_SAL_M.append(sal_m)
SAL_M.append(s_m)
num += 1
# side output
#print(len(y3))
a,b,c=self.seg_feat[num](self.pool(x1))
FF.append(a)
SEG_M.append(b)
SEG_SAL_M.append(c)
a, b = self.sal_feat[num](self.pool(x1))
#SAL_E.append(a)
SAL_M.append(b)
for i in range(6):
if i <5:
SEG_E[i]=self.up3[i](SEG_E[i])
SEG_E[i]=nn.Sigmoid()(SEG_E[i])
SAL_M[i]=self.up2[i](SAL_M[i])
SAL_M[i] = nn.Sigmoid()(SAL_M[i])
SEG_SAL_M[i] = self.up5[i](SEG_SAL_M[i])
SEG_SAL_M[i] = nn.Sigmoid()(SEG_SAL_M[i])
SEG_M[i] = self.up4[i](SEG_M[i])
SEG_M[i] = nn.functional.softmax(SEG_M[i],dim=1)
return FF,SEG_M,SEG_E,SEG_SAL_M,SAL_M
###########edge
if label=='edge':
num=0
for k in range(len(self.base)):
#print(k)
x2 = self.base[k](x2)
if k in self.e_extract:
xx2.append(x2)
#edges.append()
#print(k,x.size())
if k in self.extract:
if num<2:
edge =self.e_feat[num](xx2[2*num],xx2[2*num+1])
else:
edge = self.e_feat[num](xx2[num*3-2],xx2[num*3-1],xx2[num*3])
EDGES.append(edge)
num+=1
for i in range(5):
EDGES[i] = self.up[i](EDGES[i])
e_f = torch.cat([EDGES[0], EDGES[1], EDGES[2], EDGES[3], EDGES[4]], 1)
EDGES.append(self.fuse(e_f))
for i in range(6):
EDGES[i] = nn.Sigmoid()(EDGES[i])
return EDGES
if label == 'sal':
num=0
for k in range(len(self.base)):
x1 = self.base[k](x1)
if k in self.e_extract:
xx.append(x1)
# edges.append()
# print(k,x.size())
if k in self.extract:
if num < 2:
edge = self.e_feat[num](xx[2 * num], xx[2 * num + 1])
else:
edge = self.e_feat[num](xx[num * 3 - 2], xx[num * 3 - 1], xx[num * 3])
# edges.append(edge)
(Y, seg_ed, seg_m, sal_m) = self.seg_feat[num](x1, edge)
(s_e,s_m) = self.sal_feat[num](x1,seg_ed)
FF.append(Y)
SEG_SAL_M.append(sal_m)
SAL_E.append(s_e)
SAL_M.append(s_m)
num += 1
# side output
# print(len(y3))
a, b, c = self.seg_feat[num](self.pool(x1))
FF.append(a)
SEG_SAL_M.append(c)
a, b = self.sal_feat[num](self.pool(x1))
SAL_E.append(a)
SAL_M.append(b)
for i in range(6):
SAL_M[i] = self.up2[i](SAL_M[i])
SAL_M[i] = nn.Sigmoid()(SAL_M[i])
SAL_E[i] = self.up1[i](SAL_E[i])
SAL_E[i] = nn.Sigmoid()(SAL_E[i])
SEG_SAL_M[i] = self.up5[i](SEG_SAL_M[i])
SEG_SAL_M[i] = nn.Sigmoid()(SEG_SAL_M[i])
return FF,SEG_SAL_M, SAL_M,SAL_E
return (FF,EDGES,SEG_SAL_M,SAL_E,SEG_SAL_M,SEG_M)
def initialize_weights(net):
for m in net.modules():
if isinstance(m, nn.Conv2d):
m.weight.data.normal_(0, 0.05)
m.bias.data.zero_()
elif isinstance(m, nn.ConvTranspose2d):
m.weight.data.normal_(0, 0.05)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.05)
m.bias.data.zero_()
class DSE(nn.Module):
def __init__(self):
super(DSE, self).__init__()
self.net = DSS(vgg(base['dss'], 3),sal_extra_layer(),e_extract_layer(),seg_extra_layer())
def forward(self, input,label):
x = self.net(input,label)
return x
if __name__ == '__main__':
net = DSE().cuda()
net2 = D_U(C).cuda()
x = Variable(torch.rand(3,3,256,256)).cuda()
x2 = Variable(torch.rand(1,3,256,256))
x3 = Variable(torch.rand(1,3,256,256))
(FF,DEG_M,SETG_E,SEG_SAL_M,SAL_M) = net(x,label = 'seg')
#(FF,SEG_SAL_M, SAL_M,SAL_E) = net(x,label = 'sal')
SAL_E, SAL_M, SEG_M = net2(FF)
for i in FF:
print(i.shape)